domains / families / science / spatial-visualization
Plots spatial transcriptomics expression, clusters, and annotations on tissue using Squidpy and Scanpy. Use when choosing the plotter and spot size by platform fork (sc.pl.spatial / sq.pl.spatial_scatter with real scalefactors and capture diameter for spot/capture data like Visium and Slide-seq, versus molecule/segmentation overlays for imaging/FOV data like Xenium, MERFISH, and CosMx); getting the histology coordinate-frame transform right (micron<->pixel, scalefactors) so points land on the image; and avoiding the honest-visualization traps where interpolation/KDE manufactures spatial pattern not in the data, oversized markers fake tissue coverage, jet and other non-uniform colormaps distort structure, and non-metric UMAP/tSNE distances are misread as spatial conclusions.
The two skills cover the same domain (spatial transcriptomics visualization) but differ significantly in depth and intent. The first copy (OpenClaw) is a practical 'cookbook' of code snippets for common plotting tasks (basic plots, gene expression, co-expression, napari). The second copy (GPTomics) is a conceptual guide focused on 'honest visualization,' emphasizing the distinction between spot/capture data (Visium) and imaging/FOV data (Xenium/MERFISH), the importance of correct scalefactors, and avoiding artifacts like interpolation or misleading colormaps. The first copy lacks the critical platform-specific logic and honesty warnings present in the second, while the second copy is truncated and lacks the specific code examples for complex tasks like co-expression RGB plots or multi-panel figures found in the first. Combining them provides both the practical code and the necessary scientific rigor.